The increasing threat of an intentional or accidental release of toxins, in particular chemical toxins, including chemical warfare agents (CWAs) and toxic industrial chemicals (TICs) has increased public fear. The major problem in such attacks or accidents is to quickly detect toxins, including unknown toxins. To solve this problem, sensors are needed that are suitable for rapid, inexpensive, simple and effective (RISE) on-site detection in resource-limited settings enabling a comprehensive alert to known as well as unknown toxins.
The present industry standards for detection of chemical toxins are sophisticated analytical chemistry techniques and instruments like mass-spectroscopy (MS), chromatography including liquid or gas (LG/GC) and their combinations (GC-MS, LC-MS etc.). These technologies are accurate, sensitive, and approved by the Environmental Protection Agency (EPA), but they are time-consuming, costly, require well-trained technicians and a laboratory setting, and can only be used to detect known toxins.
Cellular and molecular sensors (biosensors) to detect biomarkers' responses to toxins in genetically-engineered whole cells (bioreporters) and the cellular components such as nucleic acids (polymerase chain reaction, PCR assay) and proteins (enzyme-linked immunosensor assay, ELISA) enable the detection of unknown analytes and toxins, and makes themespecially useful as broad spectrum screening tools and early warning testing methods.
Most biosensor technologies, such as enzyme-linked immunosorbent assay (ELISA), Fluorescence Resonance Energy Transfer (FRET) and bioreporter employing fluorophore dyes (labels), are label-based. Some of the limitations inherent in the use of labels are toxicity, photo bleaching, customized synthesis and conjugation. In addition, label-based sensors introduce uncertainty in measurements as they indirectly determine the concentration of analytes through a signal obtained from label-analyte conjugates.
Whole-Cell Biosensor Technologies that do not use any label (label-free) have also been developed, including Surface Plasmon resonance (SPR), Quartz Crystal Microbalance, Ion-Selective Field Effect Transistor, and Electric Cell-Substrate Impedance Sensing. One major drawback of these technologies is non-specificity.
Generally, cell-based biosensors integrate living cells directly onto the biosensor platform and can incorporate both prokaryotic (bacteria) and eukaryotic (yeast, mammalian) cells. For example, CANARY (Cellular Analysis and Notification of Antigen Risks and Yields) is a technology based on mammalian cells that has recently been developed to create PANTHER (Pathogen Notification for Threatening Environmental Releases) sensors. This system uses mammalian cells that have been transfected to stably express specific antibodies on their surface, which antibodies allow the detection of a variety of pathogens in a short period of time. Due to the use of mammalian cells, which require stringent culture conditions, the CANARY technology has only a short shelf-life of 3 days. Systems such as the CANARY therefore have reduced utility as on-site systems for early detection of biological and chemical warfare or accidental toxin release into the environment.
As an alternative to mammalian cells, yeast cells have previously been used in biosensors. Yeasts are robust single-cell organisms that share close genetic and functional resemblance with human cells. Due to their less demanding culture conditions, yeast cells enable longer shelf-lives of cell-based biosensors, thus allowing broader applicability and on-site utility.
To combine sensitive detection of known and unknown toxins with robust on-site performance, biosensors employing reliable label-free detection technology are needed.
Surface-Enhanced Raman Scattering (SERS) is the best alternative to currently used sensor technologies for monitoring of toxins. The SERS technique is a sensitive and specific tool providing label-free detection of molecules at very low concentrations and allowing the identification of molecules based on their vibrational fingerprint. The SERS effect is based on the optical properties of metal nanoparticle substrates. Upon excitation of the metal nanoparticle substrates by visible light, collective electron oscillations inside the nanoparticles (called localized surface plasmon resonance, LSPR) occur, creating an evanescent wave. The LSPR effect can be measured yielding an extinction spectrum, the maximum of which depends on the nature of the metal, shape and size of the nanoparticles, and the excitation wavelength. In order to achieve the most efficient enhancement, the correct pairing of substrate and excitation laser is critically important. The tuning of nanoparticle size, shape and inter particle spacing is critical to match laser's excitation wavelength towards achieving an ultra-sensitive design.
SERS is employed in two configurations, direct detection of known toxins or the SERS assays to detect nucleic acid or protein biomarkers in response to toxins enabling detection of known as well as unknown toxins.
Cellular proteins involved in stress responses are useful candidates for SERS detection of environmental toxins, because metal nanoparticles can be functionalized with receptors to specific cellular stress proteins and the presence and quantification of such cellular stress proteins in cells exposed to environmental samples are indicative of the presence and concentration of toxins. For example, RAD54 is a key cellular stress protein involved in homologous recombination and DNA repair in many organisms including human and yeast. The RAD54 protein gives a general stress response to all genotoxins and many other diverse classes of toxins, which supports a wide market of the on-chip SLISA developed in this project. A comprehensive screening validation program towards developing a commercial GreenScreen bioreporter assay using Yeast and RAD54 support the global response of our design. Further, exposure-response (ER) relationship curve developed using on-chip SLISA is correlated with EPA's databases on guideline levels of the toxins and risk characterization that will help translation of the information from any toxin, known as well as unknown, allowing global sensing.